Real-world reinforcement learning in multi-agent systems

We investigate how multi-agent learning can enable safe deployment and evaluation of autonomous systems operating in safety-critical, mixed human-robot settings. Using a case study of a 100-vehicle, real-world deployment of RL-based traffic-smoothing autonomous vehicles (AVs), we discuss the challenges of estimating when a controller will successfully bridge the sim-to-real gap.

We then discuss our work on building human-like, capable simulated agents using regularized self-play techniques. Finally, we discuss some of the challenges of MARL at scale and the new simulators we are designing to address them.